Computer Engineering Department

Mathematics II(ING107)

Content
Course Code Course Name Semester Theory Practice Lab Credit ECTS
ING107 Mathematics II 2 4 2 0 3 7
Prerequisites
Admission Requirements
Language of Instruction French
Course Type Compulsory
Course Level Bachelor Degree
Course Instructor(s)
Assistant
Objective This course deals in depth with the subject of linear algebra. Linear algebra is the basis of many techniques used in many fields such as computer science, automata and economics. Throughout the course, the basic concepts of linear algebra will be explored with an emphasis on real Euclidean spaces and vector spaces of polynomials.

In this context, the objectives of the course are:

- Introduce students to all the axiomatic definitions and signs of linear algebra: group, vector space, matrix ...
- Teach students a number of simple computational techniques that will facilitate solving linear algebra problems: solving a linear system, factoring a polynomial, simplifying a rational fraction, inverting a matrix.
- Explain the concept of dimension and its properties in a vector space.
- Show students the link between a linear function and its different matrix representations.
Content 1. Geometry of the plane and the space: Collinearity / orthogonality of the vectors of R ^ 2 or R ^ 3.
2. Geometry of the plane and the space: Application to the study to the study of the lines of the plane / of the lines and the planes of space
3. Linear systems: Gaus pivot method for solving linear systems. Geometric interpretation for systems with 2 or 3 unknowns. Discussion of the solutions of a system with parameters
4. Matrices: Definition and properties of operations on matrices. Matrix writing of a linear system. Reversible matirces. Linear application associated with a matrix.
5. Complex numbers: Cartesian and polar representation of a complex. Application to geometry and trigonometry
6. Complex numbers: Equation of degree 2 with complex coefficients. Nth roots of a complex.
7. Polynomials: Operations on polynomials. Euclidean division Roots of a polynomial
8. Partial examination / Arasinav
9. Polynomials: Taylor formulas. Factoring on C and on R
10. Vector spaces: Definition, examples and properties. Vector subspace of a vector space.
11. Vector spaces: Free families, generating families and bases of a vector space.
12. Vector Spaces: Dimensional theory.
13. Linear applications: Definition and properties. Matrix representation of a linear application.
14. Linear applications: Kernel and image of a linear application. Rank theorem. Change of bases.
Course Learning Outcomes The student who successfully completes this course will have skills in the following subjects:

1.solve a system of linear equations with the Gauss method and geometrically interpret the set of solutions,
2.use Euclidean geometry in dimension 2 or 3 to solve a geometry problem,
3.use complex numbers and their geometric representation to factorize a polynomial,
4.Factorize a polynomial irreducibly or simplify a rational fraction,
5.prove that a set is a vector space and determine its dimension,
6.determine if any subspaces of a given vector space are supplementary,
7. prove that an application is linear and write its matrix in given bases,
11. find the kernel and the image of a given linear function,
Teaching and Learning Methods Lectures and supervised works/tutorials
References 1. Lectures notes ans worksheets
2. http://braise.univ-rennes1.fr/braise.cgi
3. http://www.unisciel.fr
Print the course contents
Weekly Contents
Theory Topics
Week Weekly Contents
1 1- Geometry. Determinant in R^2
2 Vector product and determinant in R^3.  Lines and planes of space
3 2- Linear systems. Gaussian pivot method
4 3- Matrices Definition, operations
5 Invertible matrices
6 4- Complex numbers Cartesian representation, polar representation
7 nth roots of unity
8 Mid-term exams
9 5- Polynomials Definition, operations, Euclidean division
10 Taylor formula.  Factorization
11 6- Vector spaces. Definition, examples. Linear subspaces
12 Linearly independent or spanning set of vectors. Basis.
13 Dimension of a vector space
14 7- Linear applications Definition, examples. Matrix representation
Practice Topics
Week Weekly Contents
1 1- Geometry. Determinant in R^2
2 Vector product and determinant in R^3.  Lines and planes of space
3 2- Linear systems Gaussian pivot method
4 3- Matrices. Definition, operations
5 Invertible matrices
6 4- Complex numbers Cartesian representation, polar representation
7 nth roots of unity
8 Mid-term exams
9 5- Polynomials. Definition, operations, Euclidean division
10 Taylor formula.  Factorization
11 6- Vector spaces. Definition, examples. Linear subspaces
12 Linearly independent or spanning set of vectors. Basis.
13 Dimension of a vector space
14 7- Linear applications Definition, examples. Matrix representation
Assessment System
Contribution to Overall Grade
  Number Contribution
Toplam 0 0
In-Term Studies
  Number Contribution
Assignments 0 0
Presentation 0 0
Midterm Examinations (including preparation) 4 60
Project 0 0
Laboratory 0 0
Other Applications 0 0
Quiz 0 0
Term Paper/ Project 0 0
Portfolio Study 0 0
Reports 0 0
Learning Diary 0 0
Thesis/ Project 0 0
Seminar 0 0
Other 0 0
Toplam 4 60
Relation of Proficiency
No Program Learning Outcomes Contribution
1 2 3 4 5
1 Matematik, fizik ve mühendislik bilimlerine özgü konularda yeterli bilgi birikimi; bu alanlardaki kuramsal ve uygulamalı bilgileri, mühendislik problemlerinin modellenmesi ve çözümünde kullanabilme becerisi. X
2 Karmaşık bilgisayar mühendisliği problemlerini saptama, tanımlama, formüle etme ve çözme becerisi; bu amaçla uygun analiz ve modelleme yöntemlerini seçme ve uygulama becerisi. X
3 Yazılımsal veya donanımsal karmaşık bir sistemi, süreci veya donanımı gerçekçi kısıtlar ve koşullar altında, belirli gereksinimleri karşılayacak şekilde tasarlama becerisi; bu amaçla modern tasarım yöntemlerini uygulama becerisi.
4 Mühendislik uygulamalarında karşılaşılan karmaşık problemlerin analizi ve çözümü için gerekli olan modern teknik ve araçları geliştirme, seçme ve kullanma becerisi; bilişim teknolojilerini etkin bir şekilde kullanma becerisi. X
5 Analitik düşünce ile bir sistemi, sistem bileşenini ya da süreci analiz etme, modelleme, deney tasarlama ve yapma, veri toplama, çözüm algoritmaları üretebilme, uygulamaya alma ve geliştirme becerileri. X
6 Disiplin içi ve çok disiplinli takımlarda etkin biçimde çalışabilme becerisi; bireysel çalışma becerisi. X
7 Türkçe sözlü ve yazılı etkin iletişim kurma becerisi; en az iki yabancı dil bilgisi; etkin rapor yazma ve yazılı raporları anlama, yazılım ve donanım tasarımını, gerekirse teknik resim metotları kullanarak raporlayabilme, etkin sunum yapabilme becerisi. X
8 Bilgiye erişebilme ve bu amaçla kaynak araştırması yapabilme, veri tabanları ve diğer bilgi kaynaklarını kullanabilme becerisi
9 Yaşam boyu öğrenmenin gerekliliği bilinci; kendini sürekli yenileme becerisi.
10 Mesleki etik ilkelerine uygun davranma, mesleki sorumluluk bilinci; mühendislik uygulamalarında kullanılan standartlar hakkında bilgi.
11 Proje yönetimi, risk yönetimi ve değişiklik yönetimi gibi, iş hayatındaki uygulamalar hakkında bilgi; girişimcilik, yenilikçilik hakkında farkındalık; sürdürülebilir kalkınma hakkında bilgi.
12 Mühendislik uygulamalarının evrensel ve toplumsal boyutlarda sağlık, çevre ve güvenlik üzerindeki etkileri ve çağın mühendislik alanına yansıyan sorunları hakkında bilgi.
13 Bilgisayar mühendisliği uygulamalarının hukuki ve etik boyutları konusunda farkındalık.
ECTS
Activities Number Period Total Workload
Class Hours 14 10 140
Working Hours out of Class 13 5 65
Assignments 4 2 8
Midterm Examinations (including preparation) 3 4 12
Final Examinations (including preparation) 1 12 12
Quiz 5 3 15
Total Workload 252
Total Workload / 25 10.08
Credits ECTS 10
© Copyright 2016 - IT Office
Scroll to Top